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Related Experiment Videos

Nonlinear system identification using a neo fuzzy neuron algorithm: electrical drive application.

R P Landim1, B R de Menezes, S R Silva

  • 1Universitária Recife PE-Brasil. REGIS@NOVELL.CPDEE.UFMG.BR

International Journal of Neural Systems
|November 24, 1999
PubMed
Summary
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This study introduces a novel Neo-Fuzzy-Neuron algorithm for identifying nonlinear dynamic systems. The algorithm offers efficient, on-line training for rotor flux observers in induction motor drives.

Area of Science:

  • Electrical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Nonlinear dynamic systems require accurate identification for effective control.
  • Rotor flux observers are crucial for induction motor drive performance.
  • Existing identification methods may have limitations in computational cost or training requirements.

Purpose of the Study:

  • To present a Neo-Fuzzy-Neuron algorithm for nonlinear dynamic system identification.
  • To apply the algorithm as a rotor flux observer for induction motor drives.
  • To demonstrate the algorithm's efficiency and effectiveness.

Main Methods:

  • Developed a Neo-Fuzzy-Neuron algorithm.
  • Employed on-line training with gradient descent for weight adjustment.

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  • Validated through simulations and experimental results.

    • Main Results:

    • The algorithm achieves on-line training with low computational cost.
    • Convergence in one step is mathematically proven.
    • Effectiveness demonstrated for flux observation in induction motor drives.

    Conclusions:

    • The Neo-Fuzzy-Neuron algorithm is a viable tool for identifying nonlinear dynamic systems.
    • It provides an efficient and effective solution for rotor flux observation.
    • The method shows promise for real-world applications in motor drives.